Advancements in computing devices and digital video analysis technology have led to a variety of innovations in providing digital media to users. For example, digital content systems are able to analyze a digital video and metadata associated with a digital video (e.g., title, uploader identity, creator, etc.) to select semantically representative frames to use as a thumbnail for the video. Additionally, some digital content systems are able to use various shallow metrics or clustering approaches to determine which frames to use as a thumbnail for the digital video.
Despite these advances however, conventional digital content systems continue to suffer from a number of disadvantages, particularly in the accuracy, efficiency, and flexibility of generating digital video summaries that are representative of a digital video as a whole. For instance, while some conventional digital content systems can identify frames of a digital video based on metadata or shallow metrics, these systems often generate thumbnails that do not accurately represent the overall digital video. For example, many conventional digital content systems rely on administrators or others to properly tag or otherwise associate digital video with metadata that the systems can analyze to generate a thumbnail. Because of their reliance on metadata to identify frames, these conventional product recommendation systems often generate inaccurate, ineffective thumbnails. Similarly, systems that use clustering approaches do not guarantee that an accurate representation will reside in a largest cluster, thus resulting in accurate results. Moreover, conventional systems often select representative frames that contain aesthetic defects (e.g., blurry, unfocused, or unclear images). Thumbnails based on such frames also fail to convey an accurate representation of the digital video.
In addition, conventional digital content systems are also inefficient. In particular, conventional digital content systems require significant time and computing resources to analyze digital videos and select a representative thumbnail. To illustrate, some conventional digital content systems utilize an adversarial neural network to directly determine representative thumbnails. However, this approach requires significant computer processing power and time to train and subsequently apply the adversarial neural network to target digital videos. Indeed, in some circumstances, adversarial networks may not converge, and when they do, the process can take hours. Furthermore, conventional digital content systems (such as adversarial neural networks) do not scale well in producing results for larger data sets.
Moreover, some conventional digital content systems are inflexible. For example, conventional digital systems are generally limited to generating a single type of representation (e.g., single-frame thumbnails). Accordingly, these systems rigidly generate a single summary format (such as a thumbnail) and require alternative architectures or systems to generate alternative formats (such as summary videos). In addition, conventional systems that require metadata to generate thumbnails cannot flexibly adapt to scenarios where metadata or particular data structures are unavailable.
Thus, there are several disadvantages with regard to conventional digital content systems.